为机器人系统封装Florence-2模型，实现本地多模式视觉语言推理。
A ROS 2 Wrapper for Florence-2: Multi-Mode Local Vision-Language Inference for Robotic Systems
- 基于ROS 2构建三模式接口：持续订阅、同步调用、异步动作。
- 在消费级GPU上实测吞吐量，证明本地部署可行。
- 支持原生与Docker部署，输出兼容标准机器人消息格式。
基础视觉语言模型正日益被应用于机器人领域，因其能提供比专用任务流水线更丰富的语义感知。然而，其在机器人软件栈中的实际应用仍依赖于可复现的中间件集成，而非仅靠模型性能。Florence-2因其在相对可控模型规模下统一了描述生成、光学字符识别、开放词汇检测、定位等多任务，极具吸引力。本文提出一个Florence-2的ROS 2封装，通过三种互补交互模式暴露模型：持续话题驱动处理、同步服务调用和异步动作。该封装设计用于本地执行，支持原生安装与Docker容器部署，并将通用JSON输出与面向检测任务的标准ROS 2消息绑定结合。报告了功能验证及在多个GPU上的吞吐量测试，表明在消费级硬件上实现本地部署是可行的。代码库已公开：https://github.com/JEDominguezVidal/florence2_ros2_wrapper。
原文摘要 · Abstract (English)
Foundation vision-language models are becoming increasingly relevant to robotics because they can provide richer semantic perception than narrow task-specific pipelines. However, their practical adoption in robot software stacks still depends on reproducible middleware integrations rather than on model quality alone. Florence-2 is especially attractive in this regard because it unifies captioning, optical character recognition, open-vocabulary detection, grounding and related vision-language tasks within a comparatively manageable model size. This article presents a ROS 2 wrapper for Florence-2 that exposes the model through three complementary interaction modes: continuous topic-driven processing, synchronous service calls and asynchronous actions. The wrapper is designed for local execution and supports both native installation and Docker container deployment. It also combines generic JSON outputs with standard ROS 2 message bindings for detection-oriented tasks. A functional validation is reported together with a throughput study on several GPUs, showing that local deployment is feasible with consumer grade hardware. The repository is publicly available here: https://github.com/JEDominguezVidal/florence2_ros2_wrapper
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